Comprehensive Course Structure
The Business Analytics program at Prayaga Institute of Management Studies is structured over eight semesters to ensure a progressive and comprehensive learning experience. Each semester builds upon the previous one, introducing increasingly sophisticated concepts and practical applications.
| Semester | Course Code | Course Title | Credits (L-T-P-C) | Prerequisites |
|---|---|---|---|---|
| 1 | BAN-101 | Mathematics for Analytics | 3-1-0-4 | None |
| 1 | BAN-102 | Programming Fundamentals | 3-0-2-4 | None |
| 1 | BAN-103 | Introduction to Business Analytics | 3-0-0-3 | None |
| 1 | BAN-104 | Statistics and Probability | 3-1-0-4 | None |
| 2 | BAN-201 | Data Structures and Algorithms | 3-1-0-4 | BAN-102 |
| 2 | BAN-202 | Database Management Systems | 3-0-2-4 | BAN-102 |
| 2 | BAN-203 | Business Intelligence Tools | 3-0-2-4 | BAN-104 |
| 2 | BAN-204 | Financial Accounting Fundamentals | 3-0-0-3 | None |
| 3 | BAN-301 | Data Mining Techniques | 3-1-0-4 | BAN-201, BAN-202 |
| 3 | BAN-302 | Statistical Modeling | 3-1-0-4 | BAN-104 |
| 3 | BAN-303 | Business Process Management | 3-0-0-3 | BAN-204 |
| 3 | BAN-304 | Marketing Analytics | 3-0-0-3 | BAN-104 |
| 4 | BAN-401 | Predictive Modeling | 3-1-0-4 | BAN-301, BAN-302 |
| 4 | BAN-402 | Machine Learning Fundamentals | 3-1-0-4 | BAN-201, BAN-302 |
| 4 | BAN-403 | Advanced Database Systems | 3-1-0-4 | BAN-202 |
| 4 | BAN-404 | Supply Chain Analytics | 3-0-0-3 | BAN-303 |
| 5 | BAN-501 | Deep Learning and Neural Networks | 3-1-0-4 | BAN-402 |
| 5 | BAN-502 | Time Series Analysis | 3-1-0-4 | BAN-302 |
| 5 | BAN-503 | Data Visualization and Reporting | 3-0-2-4 | BAN-401 |
| 5 | BAN-504 | Ethical Analytics | 3-0-0-3 | BAN-301 |
| 6 | BAN-601 | Big Data Technologies | 3-1-0-4 | BAN-403 |
| 6 | BAN-602 | Financial Risk Analytics | 3-1-0-4 | BAN-302 |
| 6 | BAN-603 | Healthcare Data Analytics | 3-0-0-3 | BAN-301 |
| 6 | BAN-604 | Customer Analytics | 3-0-0-3 | BAN-304 |
| 7 | BAN-701 | Capstone Project I | 3-0-0-3 | BAN-501, BAN-502 |
| 7 | BAN-702 | Industry Internship Preparation | 3-0-0-3 | BAN-601 |
| 8 | BAN-801 | Capstone Project II | 4-0-0-4 | BAN-701 |
| 8 | BAN-802 | Final Industry Internship | 6-0-0-6 | BAN-702 |
Advanced Departmental Electives
Advanced departmental electives offer students opportunities to specialize in emerging areas of business analytics while maintaining flexibility to explore interdisciplinary connections.
The course Deep Learning and Neural Networks introduces students to advanced neural architectures including convolutional networks, recurrent networks, and transformers. Students learn to implement models using TensorFlow and PyTorch frameworks, gaining hands-on experience with image recognition, natural language processing, and time series forecasting applications.
Time Series Analysis delves into forecasting techniques for temporal data, covering ARIMA models, exponential smoothing, and seasonal decomposition methods. Students analyze real-world datasets including stock prices, weather patterns, and economic indicators to develop predictive models with confidence intervals.
Data Visualization and Reporting focuses on creating interactive dashboards and compelling visual narratives. Using Tableau and Power BI, students learn to design intuitive interfaces that communicate complex analytical findings effectively to diverse audiences.
The Ethical Analytics course examines the moral implications of data-driven decision-making, including privacy concerns, algorithmic bias, and responsible AI practices. Students explore regulatory frameworks such as GDPR and CCPA while developing ethical guidelines for analytics projects.
Big Data Technologies provides exposure to distributed computing platforms including Hadoop, Spark, and Kafka. Students implement large-scale data processing pipelines and learn to manage petabyte-scale datasets using cloud infrastructure.
Financial Risk Analytics combines statistical modeling with financial theory to assess credit risk, market risk, and operational risk. Students develop quantitative models for portfolio optimization and stress testing financial institutions against various scenarios.
Healthcare Data Analytics explores applications of analytics in medical research, patient outcomes, and healthcare delivery systems. Students analyze electronic health records and clinical trial data to identify patterns and improve treatment protocols.
Customer Analytics focuses on understanding consumer behavior through behavioral data, transaction history, and preference modeling. Students develop customer segmentation strategies and loyalty program designs using advanced clustering techniques.
Project-Based Learning Philosophy
Our department's philosophy on project-based learning emphasizes experiential education that bridges the gap between theoretical knowledge and practical application. Projects are designed to mirror real-world challenges, providing students with authentic learning experiences that prepare them for professional environments.
Mini-projects begin in the second year and progressively increase in complexity. These projects typically span 4-6 weeks and involve small teams working under faculty supervision. Students select from industry-sponsored problems or research topics identified by faculty members. Each project must demonstrate application of learned concepts, problem-solving capabilities, and collaborative teamwork.
The final-year thesis/capstone project represents the culmination of students' academic journey. Projects are typically undertaken in collaboration with industry partners, ensuring relevance to current market needs. Students work closely with assigned faculty mentors throughout the process, receiving guidance on methodology, analysis, and presentation skills.
Project selection involves a comprehensive evaluation process where students submit proposals outlining their intended scope, methodology, and expected outcomes. Faculty committees review these proposals to ensure alignment with program objectives and student capabilities. Selected projects may receive funding for equipment, software licenses, or travel to conferences where results can be presented.